A frequentist interpretation of probability for model-based inductive inference.
The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induc...
| Publicado en: | Synthese Vol. 190; no. 9; pp. 1555 - 1586 |
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| Formato: | Artículo |
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Springer Nature
Jun2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=87336245&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 87336245 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Jun2013 vid: 190 iid: 9 pid: 237 pub: Springer Nature artinfo: ui: 87336245 10.1007/s11229-011-9892-x ppf: 1555 ppct: 31 formats: fmt: @attributes: type: P size: 602KB tig: atl: A frequentist interpretation of probability for model-based inductive inference. aug: au: Spanos, Aris affil: Department of Economics, Virginia Tech, Blacksburg 24061 USA su: Frequentist statistics Probability theory Inductive interference Induction (Logic) Law of large numbers Underdetermination (Theory of knowledge) sug: subj: Frequentist statistics Probability theory Inductive interference Induction (Logic) Law of large numbers Underdetermination (Theory of knowledge) keyword: Circularity Duhem-Quine problem Error statistics Frequentist interpretation of probability Long-run metaphor Model-based induction Post-data severity evaluation Random samples Randomness Single event probability Strong law of large numbers ab: The main objective of the paper is to propose a frequentist interpretation of probability in the context of model-based induction, anchored on the Strong Law of Large Numbers (SLLN) and justifiable on empirical grounds. It is argued that the prevailing views in philosophy of science concerning induction and the frequentist interpretation of probability are unduly influenced by enumerative induction, and the von Mises rendering, both of which are at odds with frequentist model-based induction that dominates current practice. The differences between the two perspectives are brought out with a view to defend the model-based frequentist interpretation of probability against certain well-known charges, including [i] the circularity of its definition, [ii] its inability to assign 'single event' probabilities, and [iii] its reliance on 'random samples'. It is argued that charges [i]-[ii] stem from misidentifying the frequentist 'long-run' with the von Mises collective. In contrast, the defining characteristic of the long-run metaphor associated with model-based induction is neither its temporal nor its physical dimension, but its repeatability (in principle); an attribute that renders it operational in practice. It is also argued that the notion of a statistical model can easily accommodate non-IID samples, rendering charge [iii] simply misinformed. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Synthese is a copyright of Springer, 2013. All Rights Reserved. item: Synthese holder: Springer Nature dt: @attributes: year: 2013 holdings: @attributes: islocal: N |
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